A digital camera is a component often included in commercial electronic media device platforms. Digital cameras are now available in wearable form factors (e.g., video capture earpieces, video capture headsets, video capture eyeglasses, etc.), as well as embedded within smartphones, tablet computers, and notebook computers, etc. The transformation of image data collected by a camera module (e.g., camera sensor and optics) into values suitable for reproduction and/or display poses a challenging problem for camera control algorithms (CCA) implemented by device platforms. A computational color constancy algorithm, also known as an automatic white-balancing (AWB) algorithm, is one important part of a CCA for achieving desired color reproduction from digital cameras. The role of AWB is to estimate the chromaticity of illumination (or chromaticities in case of multiple different light sources) in terms of the response of the camera sensor color components. AWB typically entails adjustment of the intensities of the different color components to enable color reproduction that a user expects, in which the needed adjustment is highly dependent on image sensor characteristics and ambient illumination conditions at the time of capture.
Knowledge of raw image data chromaticity is advantageous for estimating the white point reliably and accurately. One technique known as the gray-edge algorithm is premised on reflections originating from the edges in a raw image data most likely being achromatic. Achromatic regions are therefore obtained from around edges within a scene. In practice, the gray-edge algorithm may require high-resolution information to be available for accurate edge information extraction, and so the accuracy of the gray-edge algorithm may be degraded significantly by a down-sampled representation (i.e. low resolution) of the raw image. Therefore, the gray-edge algorithm may not be well suited to some device platforms having limited processing capability, or operating under tight power constraints, such as most mobile device platforms. Also, the gray-edge technique does not address scenes lacking edges or matte edges and surfaces. Other conventional methods, for example employing gamut mapping, or color by correlation techniques often rely heavily on camera module characterization (CMC) information, leading them to be susceptible to CMC information errors associated with mass-production of the camera modules. CMC-intensive methods may also be computationally expensive.
Techniques for accurately estimating illumination chromaticity in terms of raw image data without strong assumptions of image content, with minimal reliance on CMC data, and without high-level image processing would therefore be advantageous, for example to improve an AWB algorithm and thereby enhance performance of digital camera platforms.